This course provides an overview of data mining and the fundamentals of using IBM SPSS Modeler (v18). The principles and practice of data mining are illustrated using the CRISP-DM methodology. The course structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The course provides training in the basics of how to read, prepare, and explore data with IBM SPSS Modeler, and introduces the student to modeling.

This course builds on the courses Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18) and Predictive Modeling for Continuous Targets Using IBM SPSS Modeler (v18). It presents advanced techniques to predict categorical and continuous targets. Before reviewing the modeling techniques, data preparation issues are addressed such as partitioning and detecting anomalies. Also, a method to reduce the number of fields to a number of core fields, referred to as components or factors, is presented. Advanced classification models, such as Decision List, Support Vector Machines and Bayes Net, are reviewed. Methods are presented to combine individual models into a single model in order to improve predictive power, including running and evaluating many models in a single run, both for categorical and continuous targets.

Clustering and Association Modeling Using IBM SPSS Modeler (v18) introduces modelers to two specific classes of modeling that are available in IBM SPSS Modeler: clustering and associations. Students will explore various clustering techniques that are often employed in market segmentation studies. Students will also explore how to create association models to find rules describing the relationships among a set of items, and how to create sequence models to find rules describing the relationships over time among a set of items.

Advanced Data Preparation Using IBM SPSS Modeler (v18) covers advanced topics to aid in the preparation of data for a successful data mining project. Students will learn how to use functions, deal with missing values, use advanced field operations, handle sequence data, apply advanced sampling methods, and improve efficiency.

Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18) (formerly Classifying Customers Using IBM SPSS Modeler) focuses on analytical models to predict a categorical field (churn, fraud, response to a mailing, pass/fail exams, machine break-down, and so forth). Students will be introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. The student will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.

This course (formerly Predicting Continuous Targets Using IBM SPSS Modeler (v16)) provides an overview of how to use IBM SPSS Modeler to predict a target field that describes numeric values. Students will be exposed to rule induction models such as CHAID and C&R Tree. They will also be introduced to traditional statistical models such as Linear Regression. Machine learning models will also be presented. Business use case examples include: predicting the length of subscription (for newspapers, telecommunication, job length, and so forth) and predicting claim amount (insurance).

This course (formerly: Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (v16)) teaches students how to analyze text data using IBM SPSS Modeler Text Analytics. You will be introduced to the complete set of steps involved in working with text data, from reading the text data to creating the final categories for additional analysis. After the final model has been created, there is an example of how to apply the model to perform churn analysis in telecommunications. Topics include how to automatically and manually create and modify categories, how to edit synonym, type, and exclude dictionaries, and how to perform Text Link Analysis and Cluster Analysis with text data. Also included are examples of how to create resource templates and Text Analysis packages to share work with other projects and other users.

Predictive Modeling with IBM SPSS Modeler demonstrates how to develop models to predict categorical and continuous outcomes, using such techniques as neural networks, decision trees, logistic regression, support vector machines, and Bayesian network models. Use of the binary classifier and numeric predictor nodes to automate model selection is included. Feature selection and detection of outliers are discussed. Expert options for each modeling node are reviewed in detail and advice is provided on when and how to use each model. Students will also learn how to combine two or more models to improve prediction.

This course will focus on how to perform Correspondence Analysis and Multi-Dimensional Scaling using procedures in the IBM SPSS Categories add-on module in IBM SPSS Statistics. Learn how to use correspondence analysis to examine the relationship of categorical data and display these relationships on perceptual maps. Learn about multidimensional scaling and preference scaling techniques to examine similarities and dissimilarities among objects such as product brands and features and customer preferences. These techniques are useful in any circumstance where you need to analyze and display graphically the correspondence among categorical data. The course will discuss the basic logic of these techniques, how to setup the analysis and examine the results using a variety of usage examples and hands-on exercises.

Introduction to IBM SPSS Statistics (v24) guides students through the fundamentals of using IBM SPSS Statistics for typical data analysis process. Students will learn the basics of reading data, data definition, data modification, and data analysis and presentation of analytical results. Students will also see how easy it is to get data into IBM SPSS Statistics so that they can focus on analyzing the information. In addition to the fundamentals, students will learn shortcuts that will help them save time. This course uses the IBM SPSS Statistics Base features.